Image compression with competing multilayer perceptrons
J. A. Sirat, J.R. Viala, Constantin Remus · International Conference on Artificial Neural Networks · 1989
Simple three-layer perceptrons with linear units working in auto-association with a reduced number of hidden units are applied to the task of digitized image compression. First, an algorithm developed using several multilayer perceptrons in competition for the coding of a TV-image is explained. A theoretical interpretation in terms of principal component analysis is also developed. Then, a study of its performances at different bit-rates is presented. This leads to an extension of the algorithm in image segmentation through texture analysis. Next, the results are compared with conventional methods: the optimal stationary process known as Karhunen-Loeve transform, and an algorithm often proposed for real-time applications, the discrete cosine transform. Finally, an estimation of hardware complexity in the case of real-time television is presented.